{"id":"W2041040308","doi":"10.1021/jm051209w","title":"Similarity Based Virtual Screening:  A Tool for Targeted Library Design","year":2006,"lang":"en","type":"article","venue":"Journal of Medicinal Chemistry","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Union of Biochemistry and Molecular Biology","keywords":"Virtual screening; Drug discovery; Chemistry; Computational biology; High-throughput screening; Similarity (geometry); Chemical library; Drug; Throughput; Small molecule; Combinatorial chemistry; Pharmacology; Computer science; Biochemistry; Biology; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001266273,0.0001618144,0.0002923106,0.00007974979,0.00008004499,0.00009820732,0.0008247205,0.00009216042,0.00005212413],"category_scores_gemma":[0.0004731791,0.0001398426,0.000200186,0.0002755014,0.00006229394,0.0006278962,0.00008097944,0.0003152053,7.026533e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004657927,"about_ca_system_score_gemma":0.0006694825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001301371,"about_ca_topic_score_gemma":2.974996e-8,"domain_scores_codex":[0.9981611,0.0001209804,0.0005851197,0.000208386,0.0006899393,0.0002344567],"domain_scores_gemma":[0.9975692,0.00143508,0.0004439148,0.0002151029,0.000204977,0.0001317026],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00202433,0.00104297,0.00259293,0.0004411262,0.0002283081,0.0008295252,0.0002077924,0.6155178,0.1623249,0.003517641,0.1546859,0.05658688],"study_design_scores_gemma":[0.003272536,0.0004745032,0.004616559,0.0002299638,0.00006273155,0.0003831205,0.00003371485,0.6241487,0.3445394,0.01451097,0.00736345,0.0003643328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03529028,0.0002588207,0.9611163,0.002738161,0.0002014756,0.00009632015,0.000007011766,0.00003984266,0.0002517525],"genre_scores_gemma":[0.3699109,0.000001959108,0.6284925,0.0005388128,0.0008742843,0.0000042987,0.000008510382,0.00001356864,0.0001551065],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3346207,"threshold_uncertainty_score":0.5702614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0271654310004807,"score_gpt":0.273841337082438,"score_spread":0.2466759060819573,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}